# wangyongjie-ntu/Awesome-explainable-AI

A  collection of research materials on explainable AI/ML

Repository: https://github.com/wangyongjie-ntu/Awesome-explainable-AI
Canonical: https://ross.abutalabs.com/products/awesome-explainable-ai
Language: Markdown
License: MIT
License Family: permissive
Topics: interpretable-ai, explainable-ai, interpretability, explanation-system, xai, xml, counterfactual-explanations, recourse
Last push: 2026-08-19T02:09:45+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 100
- inputs: {"age_days": 2390, "days_push": 15, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1649, forks 225 (observed 2026-08-28T04:05:16.744871+00:00)

## What it is
A curated awesome-list of research papers and surveys on explainable AI (XAI) and interpretable machine learning. It organizes frontier publications into categories such as surveys, counterfactual explanations, and recourse.

## Use cases
- find research papers on explainable AI
- survey the state of the art in model interpretability
- learn about counterfactual explanations and recourse methods
- find XAI surveys related to large language models
- get started researching interpretable machine learning
- keep up with new XAI publications

## When to choose
- you need a curated reading list on explainable AI research
- you are a researcher or student surveying XAI literature
- you want links to surveys covering LLM explainability

## When to avoid
- you need a runnable XAI library or tool
- you want production-ready explanation algorithms rather than papers
- you need tutorials with code rather than a paper index

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: artificial-intelligence, machine-learning, tutorials
- platform: cross-platform
- tags: awesome-list, explainable-ai, xai, interpretability, research-papers, counterfactual-explanations

## Member repositories
- wangyongjie-ntu/Awesome-explainable-AI (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:16.744871+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:45:10.921510+00:00, confidence not recorded.
  - readme: https://github.com/wangyongjie-ntu/Awesome-explainable-AI (fetched 2026-08-28T04:05:16.744871+00:00, sha 04139db60561)
- Data as of 2026-08-30T08:39:29.467469+00:00.